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Long-WAM: Scaling the Context of World-Action Models

L5 · ResearcherResearcharXiv· 10/7/2026

Presents novel architecture and training methodology for real-time robotic control with extended context windows, advancing state-of-the-art in embodied AI.

AI Summary

Long-WAM scales robot world-action models to use longer visual histories via autoregressive pretraining, achieving 78.7% success on RoboCasa GR-1 and enabling real-time deployment on Unitree G1 hardware.

Excerpt

Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control constraints. Our central finding is that access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively (AR). We first learn causal prediction from robot and egocentr

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